In a securities market, shares of stock in corporations (and options thereon), commodity futures (and options thereon), currencies, bonds and the like are commonly traded at financial market centers or exchanges. Other traded items can include, but are not limited to index tracking funds, mutual funds, derivatives, etc. For simplicity, the following discussion will be primarily in connection with the purchase and sale of corporate stock, but the principles set forth may be applied to any public or private exchange traded item.
Within a market center or exchange, traders buy and sell securities. To maximize the profit taken from the securities market, traders often track and analyze certain information to determine what moment is advantageous to sell or buy a particular security. Traditionally, traders have tracked information derived from the “floor” of exchanges such as the New York Stock Exchange (NYSE) and American Stock Exchange (ASE), electronic exchanges such as the National Association of Securities Dealers (NASDAQ), futures exchanges such as the Chicago Mercantile Exchange, private market maker exchanges, and the like. Each exchange makes available certain data to the public, and certain data to the market makers associated with the exchange, either for free or via a paid subscription service. For example, this data may include the reporting of trades, trade times, trade volumes, bids, asks, bid volumes, ask volumes, and various other transactional information or pre-transactional information, in the form of “Level I”, “Level II” and “Level III” information.
The nature of Level I, Level II and Level III information is known in the art. Briefly, Level I information for a particular security typically includes, but may not be limited to, the current trade price (i.e., last trade), the current trade volume, the total volume of shares traded during the trading session, the price to earnings (P/E) ratio, the previous trading day's closing price, the present day's opening price, the high and low to prices for the day and for the previous 52 weeks, the change from the prior closing price, the lowest ask price (inside ask), the highest bid price (inside bid), the earnings per share, the market capitalization, the dividend paid per share, the dividend yield, news items and articles whether textual, audio, or video, and so forth. Also available are records of historical performance, which can be displayed graphically on a trade by trade basis or aggregated over periods of time ranging from fractions of seconds to years. Also available are statistics for an entire exchange, such as total volume of shares traded and statistics for calculated market indices, such as the Dow-Jones Industrial Average (“The DOW”), the NASDAQ Composite, the Standard and Poor's 500 (“S&P 500”), the Russell 2000, sector indices, etc.
Level II information for a particular security typically includes each market maker, each ECN, and each regional exchange having an open (or active) bid or ask, the time when the bid or ask was placed (also referred to respectively as bid time and ask time), the size of the bid or ask (i.e., number of shares, often reported in lots of 100) and the price of the ask or bid.
Level III information for a particular security includes everything that the other two levels do, plus it allows a particular market maker to go into an exchange's system and change his bids and offers and size on the stocks in which he makes a market or places an order.
This information can be transmitted electronically in near real time (i.e., almost simultaneously with actual market activity) to computer workstations or computer servers for traders and/or computerized trading systems to view and analyze. Trading information also may be recorded and accessed subsequently for at least a given period of time, sometimes, depending on the source, going back days, months, or years. Such information commonly is referred to in the art as Electronic Historical Financial Data (EHFD).
The practice of saving EHFD information is well established. EHFD is typically transactional in basis, and contains a record of trades that have occurred over a defined period of time. Each tradable financial security is typically kept in its own separate data file. For example, a separate data file would be kept for Microsoft trades apart from, Home Depot trades. This practice is similarly used for various other types of financial securities, instruments, and indices with separate files for different items that are traded whether a given item be a stock, a bond, or a futures contract, or index.
EHFD is typically saved in files consisting of formatting information and data records. Each record in the file typically contains various fields of information for a specific interval of time. The granularity of the time interval can be as small as a record for every trade. Other typical time granularities for the records are 1 minute, 5 minute, 10 minute, 15 minute, 30 minute, 60 minute, day, week and month. Custom time interval aggregations can also be kept. The record fields are typically security symbol, delivery month, option expiration, date, time, open (first trade), highest, lowest, close (last trade) and trade volume.
Relatedly, financial market data may be run through an Analytics and Charting Software Program (ACSP) for analyzing and displaying market information, and creating analytic data. Analytic data is derived from and used in conjunction with financial data. Analytic data is derived by applying various formulae and algorithms to the types of data as described above. Such analytic data is well known in the art. It is vast in scope, and typically provides for a means for user customization by allowing the user to change the parameter values of the formula and algorithms. Since time is usually of the essence in trading, most analytic data is calculated and updated in real time to reflect the current market. The process of updating of analytics as new financial information arrives is complex. The updating process often requires the use of intermediate variables. Intermediate variables are analytic data themselves, but are typically only interesting in that they are necessary to update other analytic data. Analytic data goes by many names, known within the art, depending upon how it is used: indicator, index, study, system, system indicator, or alert criterion. The common feature of all analytic data is that it is data derived from the raw financial data.
An example of an analytic data is a simple moving average. A specific analytic data point might be the average of the last five (5) trades of shares of Microsoft at 12:37:40 PM on Sept. 18 2009. To compute this data point from a table of trades involves identifying the trades and summing the last 5 trade prices. The sum is an example of an intermediate variable, as are each of the trades in the sum. The average is obtained by dividing the sum by 5. To determine this same average a moment later, after a new trade has occurred, one may subtract the oldest trade from the sum, add the new trade to the sum, and divide the sum by 5. Such a method of obtaining new information, and updating an analytic, possibly using its previous value, and possibly using intermediate variables, is known in the art as an update formulae or update algorithm.
ACSP tools are known in the art and are utilized in connection with the receipt, analysis, and display of real time and/or historical financial market data. An ACSP may derive and display analytics, bid/ask, trade, and price information from the Level I and/or Level II data for one or more securities. Such market information, and analytics may be useful to traders by providing indications of price or other trends for the financial securities.
In addition to market information provided by the exchanges, a variety of news sources also generate information, whether textual, audio, or video, that may be relevant to securities trading. News items such as earnings reports, mergers and acquisitions reports, product press releases, SEC filings and various other newsworthy events may occur for numerous securities on any given day. Such events may be reported in a variety of print, broadcast (e.g. television and radio), Internet, and other media.
Another type of information which traders find useful for communication and training is user experience information. It is not uncommon for traders to gather as clubs, or in forums to discuss their views on the markets, and their trading ideas and experiences. It is also not uncommon for traders to spend significant amount of money for training, advice, both on the markets, and on methods of trading, and various trading tools and resources. The user experience in a typical ACSP is quite configurable, and the outcome of a trading session can be enormously dependent upon how the available resources of the ACSP are initially configured, and then how they are subsequently manipulated by the user during trading. Traders typically wish to avoid, and if they cannot avoid, avoid repeating, a particularly disastrous trading experience. The actual, intended, or mistaken user actions during a trading session can be useful for communication and training. Being able to capture these trading sessions may be of enormous benefit to a trader. Once captured, this user experience information can be edited, recorded, and shared. After-the- fact narrative and active user metadata may also be added. Collectively, user experience data and user metadata greatly broaden the scope of what an ACSP can do and greatly deepen the usefulness of the user experience.
Many traders are interested in short term upward or downward price movements for selected securities, looking for perceived market imbalances, and often executing numerous trades in any given trading day. Because of these market imbalances, sometimes opportunities exist to make a significant profit over a very short time frame. There are many different events that can cause a market imbalance. The following are a few of many possible examples. Some imbalances may exist because a large position must be liquidated quickly causing supply to temporarily exceed demand. Some market imbalances may exist because a news report was erroneous. This can cause ill-informed market participants to mis-value the security. Other market imbalances may exist because of stale information. For example, the current valuation of the security reflects an assumed cost of a competing or complementary product which has changed. A market imbalance may simply occur because the security is only being initially offered and has not had an opportunity to weigh in market sentiment. Some kinds of market imbalances resolve quickly, such as erroneous news. Others may be caused by governmental policies which may continue for years.
Predicting upward and downward price movement, however, is difficult to say the least. To profit from such perceived imbalances, the trader needs to know whether patterns of imbalance repeat and can be profitable. To do so, traders seek to become proficient in both the creation and the execution of a trading strategy which encompass the identification and contingent execution of trades. Since the identification, estimation of profit, and estimate of time-frame are contingent on ones understanding of the underlying market imbalance and on the behavior of the other market participants, traders are continuously looking to develop or modify their trading strategies and trading techniques. Despite the continuous effort to develop new and better trading strategies, there is no known convenient and effective way to test whether any given strategy will be likely to succeed under actual real time market conditions. Of course, a trader may simply start trading in accordance with the strategy to see if it works, but, given the complexities of the markets, which are virtually innumerable, one would do so at great financial risk since each dollar may be lost only once.
Conventionally, one of the primary methods used by traders to test and validate a trading strategy is to “paper trade”. Typically a trader would paper trade by using EHFD files with a computer software program designed to back test trading strategies. A trader would run their analysis only on a predetermined market or security, or only on groups of predetermined markets or securities. A trader will create a “dummy” account with dummy cash and dummy credit balances. A dummy account permits a trader to execute simulated trades without using real money. In this manner, after a testing period (which may comprise minutes, hours, days, months, etc.), a trader can determine if he or she is able to identify a pattern, execute the related trades, and determine whether or not the trading strategy made enough money to justify the risks had the simulated trades actually been executed.
Although this testing method can determine whether the trading strategy would have succeeded or failed during the test period, little confidence can be ascribed to the outcome. For example, a trader may not be able to determine what aspects of the strategy led to success or failure, or whether some market factor unusual to the test period affected the efficacy of the strategy. The use of “dummy” accounts, therefore, has proven deficient insofar as a trader may not attain the necessary information to analyze a trading strategy, and to adjust the execution of the strategy as warranted.
Using conventional trading systems, a trader could not effectively develop a benchmark for their trading ideas from which they could practice with the exact same market conditions. Regardless of the results they got, since they were using EHFD files, one at a time, a trader could never develop an understanding of the interactive, collective, cause and effect forces that exist in the real market. The existing state of the art, and methods of looking back at just on EHFD file at a time, does not capture how financial markets actually work.
Because of their dynamic and ever changing nature, financial markets are elusive. They never exactly repeat themselves. A trader has not been able to effectively reproduce and practice his ideas and trading techniques on the exact same conditions and interdependencies across a market. Furthermore, much of trading depends upon the non-automatic action of the trader himself or herself. Where he or she focused, what markets were being examined, what analytics were employed, what heuristic judgments were made, and what actions were taken, all depend in a sensitive way on the user experience. Having an exact record of what the market did is only a part of what happened. The other part is having a record of what the trader did.
There have been previous attempts to capture and replay market activity, but they have proven deficient in providing traders content in a format usable to improve their trading strategies. They have not provided a trader with an observation window into the detailed and collective bidding process that takes place prior to the actual trade taking place. The NASDAQ has created “NASDAQ Market Replay”. However, NASDAQ Market Replay is very limited in scope. It allows a user to select a one particular stock registered on the NASDAQ and to select a date and a time span on that date for which to replay market activity. The user is then presented with all of the information that was transmitted by the NASDAQ for that stock only and to replay it.
There have also been other attempts to capture and replay market activity, but these have all been limited in scope to only a few shares at a time. Conventional capture and replay systems have not been able to capture and replay entire markets at one time. Some have captured a limited amount of user experience data such as the analytics applied. Video recordings of actual trading have been made. Prior to this invention, nothing has captured the full user trading experience in a communicable, and persistent way, with the sufficient detail and fidelity needed by a student in the art of trading.